




'''simple docstring'''







import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


lowerCAmelCase_     						=				logging.getLogger(__name__)
lowerCAmelCase_     						=				list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCAmelCase_     						=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class 	_snake_case:
 __snake_case:							Optional[str]						=					field(
     default=UpperCAmelCase       , metadata={
         '''help''': (
             '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
         )
     }       , )
 __snake_case:							Optional[str]						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCAmelCase      )}       , )
 __snake_case:							Optional[str]						=					field(
     default=UpperCAmelCase       , metadata={
         '''help''': (
             '''Override some existing default config settings when a model is trained from scratch. Example: '''
             '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
         )
     }       , )
 __snake_case:							Optional[str]						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}      )
 __snake_case:							Optional[str]						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}      )
 __snake_case:							Optional[str]						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       , )
 __snake_case:							bool						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}       , )
 __snake_case:							str						=					field(
     default='''main'''       , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}       , )
 __snake_case:							bool						=					field(
     default=UpperCAmelCase       , metadata={
         '''help''': (
             '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
             '''with private models).'''
         )
     }       , )






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   List[str]:






    """simple docstring"""


    if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
       raise ValueError(
           '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'    )




@dataclass
class 	_snake_case:
 __snake_case:							Optional[str]						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}      )
 __snake_case:							Optional[str]						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}      )
 __snake_case:							Optional[str]						=					field(default=UpperCAmelCase       , metadata={'''help''': '''The input training data file (a text file).'''}      )
 __snake_case:							Optional[str]						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}       , )
 __snake_case:							Optional[str]						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}       , )
 __snake_case:							Optional[str]						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}       , )
 __snake_case:							bool						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}      )
 __snake_case:							Optional[int]						=					field(
     default=5       , metadata={
         '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
     }       , )
 __snake_case:							Optional[int]						=					field(
     default=UpperCAmelCase       , metadata={
         '''help''': (
             '''The maximum total input sequence length after tokenization. Sequences longer '''
             '''than this will be truncated. Default to the max input length of the model.'''
         )
     }       , )
 __snake_case:							Optional[int]						=					field(
     default=UpperCAmelCase       , metadata={'''help''': '''The number of processes to use for the preprocessing.'''}       , )
 __snake_case:							float						=					field(
     default=0.15       , metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}      )
 __snake_case:							bool						=					field(
     default=UpperCAmelCase       , metadata={
         '''help''': (
             '''Whether to pad all samples to `max_seq_length`. '''
             '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
         )
     }       , )






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   int:






    """simple docstring"""


    if self.train_file is not None:
       A__       =     self.train_file.split('.'    )[-1]
       assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
    if self.validation_file is not None:
       A__       =     self.validation_file.split('.'    )[-1]
       assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase      ,'r'      ,encoding='utf-8'						) as f:
      A__       =     [json.loads(UpperCAmelCase						) for line in f.read().splitlines() if (len(UpperCAmelCase						) > 0 and not line.isspace())]
   assert len(UpperCAmelCase						) == len(UpperCAmelCase						)

   A__       =     {c: dataset[c] for c in dataset.column_names}
   A__       =     refs
   return Dataset.from_dict(UpperCAmelCase						)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
   if len(sys.argv						) == 2 and sys.argv[1].endswith('.json'						):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      A__  ,			A__  ,			A__       =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
   else:
      A__  ,			A__  ,			A__       =     parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   A__       =     None
   if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
      A__       =     get_last_checkpoint(training_args.output_dir						)
      if last_checkpoint is None and len(os.listdir(training_args.output_dir						)						) > 0:
         raise ValueError(
             F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
             'Use --overwrite_output_dir to overcome.'						)
      elif last_checkpoint is not None:
         logger.info(
             F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
             'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'						)

    # Setup logging
   logging.basicConfig(
       format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'      ,datefmt='%m/%d/%Y %H:%M:%S'      ,handlers=[logging.StreamHandler(sys.stdout						)]      ,)
   logger.setLevel(logging.INFO if is_main_process(training_args.local_rank						) else logging.WARN						)

   # Log on each process the small summary:
   logger.warning(
       F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
       + F"""distributed training: {bool(training_args.local_rank != -1						)}, 16-bits training: {training_args.fpaa}"""						)
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank						):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
   logger.info('Training/evaluation parameters %s'      ,UpperCAmelCase						)

   # Set seed before initializing model.
   set_seed(training_args.seed						)

   # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
   # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
   # (the dataset will be downloaded automatically from the datasets Hub).
   #
   # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
   # 'text' is found. You can easily tweak this behavior (see below).
   #
   # In distributed training, the load_dataset function guarantee that only one local process can concurrently
   # download the dataset.
   if data_args.dataset_name is not None:
      # Downloading and loading a dataset from the hub.
      A__       =     load_dataset(data_args.dataset_name      ,data_args.dataset_config_name						)
      if "validation" not in datasets.keys():
         A__       =     load_dataset(
             data_args.dataset_name      ,data_args.dataset_config_name      ,split=F"""train[:{data_args.validation_split_percentage}%]"""      ,)
         A__       =     load_dataset(
             data_args.dataset_name      ,data_args.dataset_config_name      ,split=F"""train[{data_args.validation_split_percentage}%:]"""      ,)
   else:
      A__       =     {}
      if data_args.train_file is not None:
         A__       =     data_args.train_file
      if data_args.validation_file is not None:
         A__       =     data_args.validation_file
      A__       =     data_args.train_file.split('.'						)[-1]
      if extension == "txt":
         A__       =     'text'
      A__       =     load_dataset(UpperCAmelCase      ,data_files=UpperCAmelCase						)
   # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
   # https://huggingface.co/docs/datasets/loading_datasets.html.

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   A__       =     {
       'cache_dir': model_args.cache_dir,
       'revision': model_args.model_revision,
       'use_auth_token': True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      A__       =     AutoConfig.from_pretrained(model_args.config_name      ,**UpperCAmelCase						)
   elif model_args.model_name_or_path:
      A__       =     AutoConfig.from_pretrained(model_args.model_name_or_path      ,**UpperCAmelCase						)
   else:
      A__       =     CONFIG_MAPPING[model_args.model_type]()
      logger.warning('You are instantiating a new config instance from scratch.'						)
      if model_args.config_overrides is not None:
         logger.info(F"""Overriding config: {model_args.config_overrides}"""						)
         config.update_from_string(model_args.config_overrides						)
         logger.info(F"""New config: {config}"""						)

   A__       =     {
       'cache_dir': model_args.cache_dir,
       'use_fast': model_args.use_fast_tokenizer,
       'revision': model_args.model_revision,
       'use_auth_token': True if model_args.use_auth_token else None,
   }
   if model_args.tokenizer_name:
      A__       =     AutoTokenizer.from_pretrained(model_args.tokenizer_name      ,**UpperCAmelCase						)
   elif model_args.model_name_or_path:
      A__       =     AutoTokenizer.from_pretrained(model_args.model_name_or_path      ,**UpperCAmelCase						)
   else:
      raise ValueError(
          'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
          'You can do it from another script, save it, and load it from here, using --tokenizer_name.'						)

   if model_args.model_name_or_path:
      A__       =     AutoModelForMaskedLM.from_pretrained(
          model_args.model_name_or_path      ,from_tf=bool('.ckpt' in model_args.model_name_or_path						)      ,config=UpperCAmelCase      ,cache_dir=model_args.cache_dir      ,revision=model_args.model_revision      ,use_auth_token=True if model_args.use_auth_token else None      ,)
   else:
      logger.info('Training new model from scratch'						)
      A__       =     AutoModelForMaskedLM.from_config(UpperCAmelCase						)

   model.resize_token_embeddings(len(UpperCAmelCase						)						)

   # Preprocessing the datasets.
   # First we tokenize all the texts.
   if training_args.do_train:
      A__       =     datasets['train'].column_names
   else:
      A__       =     datasets['validation'].column_names
   A__       =     'text' if 'text' in column_names else column_names[0]

   A__       =     'max_length' if data_args.pad_to_max_length else False

   def tokenize_function(UpperCAmelCase						):
      # Remove empty lines
      A__       =     [line for line in examples['text'] if len(UpperCAmelCase						) > 0 and not line.isspace()]
      return tokenizer(examples['text']      ,padding=UpperCAmelCase      ,truncation=UpperCAmelCase      ,max_length=data_args.max_seq_length						)

   A__       =     datasets.map(
       UpperCAmelCase      ,batched=UpperCAmelCase      ,num_proc=data_args.preprocessing_num_workers      ,remove_columns=[text_column_name]      ,load_from_cache_file=not data_args.overwrite_cache      ,)

   # Add the chinese references if provided
   if data_args.train_ref_file is not None:
      A__       =     add_chinese_references(tokenized_datasets['train']      ,data_args.train_ref_file						)
   if data_args.validation_ref_file is not None:
      A__       =     add_chinese_references(
          tokenized_datasets['validation']      ,data_args.validation_ref_file						)
   # If we have ref files, need to avoid it removed by trainer
   A__       =     data_args.train_ref_file or data_args.validation_ref_file
   if has_ref:
      A__       =     False

   # Data collator
   # This one will take care of randomly masking the tokens.
   A__       =     DataCollatorForWholeWordMask(tokenizer=UpperCAmelCase      ,mlm_probability=data_args.mlm_probability						)

   # Initialize our Trainer
   A__       =     Trainer(
       model=UpperCAmelCase      ,args=UpperCAmelCase      ,train_dataset=tokenized_datasets['train'] if training_args.do_train else None      ,eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None      ,tokenizer=UpperCAmelCase      ,data_collator=UpperCAmelCase      ,)

   # Training
   if training_args.do_train:
      if last_checkpoint is not None:
         A__       =     last_checkpoint
      elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path						):
         A__       =     model_args.model_name_or_path
      else:
         A__       =     None
      A__       =     trainer.train(resume_from_checkpoint=UpperCAmelCase						)
      trainer.save_model()  # Saves the tokenizer too for easy upload

      A__       =     os.path.join(training_args.output_dir      ,'train_results.txt'						)
      if trainer.is_world_process_zero():
         with open(UpperCAmelCase      ,'w'						) as writer:
            logger.info('***** Train results *****'						)
            for key, value in sorted(train_result.metrics.items()						):
               logger.info(F"""  {key} = {value}"""						)
               writer.write(F"""{key} = {value}\n"""						)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir      ,'trainer_state.json'						)						)

    # Evaluation
   A__       =     {}
   if training_args.do_eval:
      logger.info('*** Evaluate ***'						)

      A__       =     trainer.evaluate()

      A__       =     math.exp(eval_output['eval_loss']						)
      A__       =     perplexity

      A__       =     os.path.join(training_args.output_dir      ,'eval_results_mlm_wwm.txt'						)
      if trainer.is_world_process_zero():
         with open(UpperCAmelCase      ,'w'						) as writer:
            logger.info('***** Eval results *****'						)
            for key, value in sorted(results.items()						):
               logger.info(F"""  {key} = {value}"""						)
               writer.write(F"""{key} = {value}\n"""						)

   return results







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   main()


if __name__ == "__main__":
    main()








'''simple docstring'''







from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
    import tensorflow as tf

    from transformers import TFBlipTextModel
    from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 	_snake_case:






 def __init__(self       :  Any    ,		a       :  str    ,		a       :  Union[str, Any]=12    ,		a       :  List[str]=7    ,		a       :  Dict=True    ,		a       :  Tuple=True    ,		a       :  Any=True    ,		a       :  Optional[Any]=99    ,		a       :  Optional[Any]=32    ,		a       :  Tuple=32    ,		a       :  List[Any]=2    ,		a       :  str=4    ,		a       :  Dict=37    ,		a       :  Optional[Any]=0.1    ,		a       :  List[Any]=0.1    ,		a       :  Dict=5_12    ,		a       :  List[Any]=0.02    ,		a       :  Any=0    ,		a       :  Optional[int]=None    ,		)       ->   List[Any]:






    """simple docstring"""


    A__       =     parent
    A__       =     batch_size
    A__       =     seq_length
    A__       =     is_training
    A__       =     use_input_mask
    A__       =     use_labels
    A__       =     vocab_size
    A__       =     hidden_size
    A__       =     projection_dim
    A__       =     num_hidden_layers
    A__       =     num_attention_heads
    A__       =     intermediate_size
    A__       =     dropout
    A__       =     attention_dropout
    A__       =     max_position_embeddings
    A__       =     initializer_range
    A__       =     scope
    A__       =     bos_token_id






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   List[Any]:






    """simple docstring"""


    A__       =     ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size    )

    A__       =     None
    if self.use_input_mask:
       A__       =     random_attention_mask([self.batch_size, self.seq_length]    )

    if input_mask is not None:
       A__       =     input_mask.numpy()
       A__  ,			A__       =     input_mask.shape
       A__       =     np.random.randint(1    ,		seq_length - 1    ,		size=(batch_size,)    )
       for batch_idx, start_index in enumerate(a    ):
          A__       =     1
          A__       =     0

    A__       =     self.get_config()

    return config, input_ids, tf.convert_to_tensor(a    )






 def 						_UpperCamelCase (self       :  Tuple    )       ->   Union[str, Any]:






    """simple docstring"""


    return BlipTextConfig(
        vocab_size=self.vocab_size    ,		hidden_size=self.hidden_size    ,		projection_dim=self.projection_dim    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		dropout=self.dropout    ,		attention_dropout=self.attention_dropout    ,		max_position_embeddings=self.max_position_embeddings    ,		initializer_range=self.initializer_range    ,		bos_token_id=self.bos_token_id    ,		)






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  Any    ,		a       :  List[Any]    ,		a       :  Union[str, Any]    )       ->   Any:






    """simple docstring"""


    A__       =     TFBlipTextModel(config=a    )
    A__       =     model(a    ,		attention_mask=a    ,		training=a    )
    A__       =     model(a    ,		training=a    )
    self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)    )
    self.parent.assertEqual(result.pooler_output.shape    ,		(self.batch_size, self.hidden_size)    )






 def 						_UpperCamelCase (self       :  str    )       ->   List[Any]:






    """simple docstring"""


    A__       =     self.prepare_config_and_inputs()
    A__  ,			A__  ,			A__       =     config_and_inputs
    A__       =     {'input_ids': input_ids, 'attention_mask': input_mask}
    return config, inputs_dict




@require_tf
class 	_snake_case(			UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Optional[int]						=					(TFBlipTextModel,) if is_tf_available() else ()
 __snake_case:							Union[str, Any]						=					False
 __snake_case:							Any						=					False
 __snake_case:							Union[str, Any]						=					False






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   List[Any]:






    """simple docstring"""


    A__       =     BlipTextModelTester(self    )
    A__       =     ConfigTester(self    ,		config_class=a    ,		hidden_size=37    )






 def 						_UpperCamelCase (self       :  Tuple    )       ->   str:






    """simple docstring"""


    self.config_tester.run_common_tests()






 def 						_UpperCamelCase (self       :  Any    )       ->   Dict:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   Dict:






    """simple docstring"""


    pass






 def 						_UpperCamelCase (self       :  str    )       ->   int:






    """simple docstring"""


    pass






 @unittest.skip(reason='Blip does not use inputs_embeds'    )
 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   str:






    """simple docstring"""


    pass






 @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'    )
 def 						_UpperCamelCase (self       :  int    )       ->   List[Any]:






    """simple docstring"""


    pass






 @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'    )
 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    pass






 @slow
 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       A__       =     TFBlipTextModel.from_pretrained(a    )
       self.assertIsNotNone(a    )






 def 						_UpperCamelCase (self       :  str    ,		a       :  Optional[int]=True    )       ->   List[Any]:






    """simple docstring"""


    super().test_pt_tf_model_equivalence(allow_missing_keys=a    )


